Academic Editors: Vitor Caldeirinha,
João Correia, Maria Teresa Folgôa
Batista and Tiago Pinho
Received: 27 November 2024
Revised: 26 December 2024
Accepted: 30 December 2024
Published: 4 January 2025
Citation: Gonçalves, M.; Salgado, C.;
de Sousa, A.; Teixeira, L. Data
Storytelling and Decision-Making in
Seaport Operations: A New Approach
Based on Business Intelligence.
Sustainability 2025, 17, 337. https://
doi.org/10.3390/su17010337
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
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(https://creativecommons.org/
licenses/by/4.0/).
Article
Data Storytelling and Decision-Making in Seaport Operations:
A New Approach Based on Business Intelligence
Marco Gonçalves
1
, Cátia Salgado
2
, Amaro de Sousa
3
and Leonor Teixeira
1,4,
*
1
Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro,
3810-193 Aveiro, Portugal; marcofgoncalves@ua.pt
2
Administração dos Portos de Sines e do Algarve (APS), 7521-953 Sines, Portugal;
catia.salgado@apsinesalgarve.pt
3
Institute of Telecommunications—Pole of Aveiro (IT), Department of Electronics, Telecommunications and
Informatics (DETI), University of Aveiro, 3810-193 Aveiro, Portugal; asou@ua.pt
4
Intelligent Systems Associate Laboratory (LASI), Institute of Electronics and Informatics Engineering of
Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal
* Correspondence: lteixeira@ua.pt; Tel.: +351-234370361
Abstract: Seaports are experiencing several challenges due to the explosive growth of the
maritime shipping business, which has led to the need for digitalized operations and more
effective solutions. This article provides a comprehensive exploration of the process used to
create a reliable business intelligence solution by analyzing the container delivery and pick-
up services flow in one of Portugal’s largest maritime container ports, using the CRISP-DM
methodology. The solution, built with Microsoft Power BI
®
, provides the capability to
identify and address data anomalies and present key performance indicators in visually
dynamic dashboards. This solution empowers stakeholders to gain invaluable insights into
the current and future operational status, thereby facilitating well-informed and adaptable
decision-making, representing the main practical contributions. As a theoretical contri-
bution, this study advances research by covering a gap in the literature and establishing
the foundations for future business intelligence applications within the maritime industry,
with a focus on addressing data dispersion challenges, enhancing logistics flow analysis,
and reducing port congestion. The manuscript is structured into seven sections: introduc-
tion, literature review, port challenges, methodology, tool development, SWOT analysis,
and conclusion.
Keywords: business intelligence; performance indicators; dashboard; CRISP-DM; seaports
1. Introduction
Maritime transport is a critical component of the global economy, facilitating the
movement of 12 billion tons of goods in 2022 [
1
]. A significant portion of these goods is
transported in containers, which offer a versatile and efficient means of moving products
across various modes of transportation, such as ships, trucks, and trains. The demand for
container transport is expected to grow, with a projected annual increase of 3% in container
volume from 2024 to 2028 [2].
This growing reliance on maritime transportation comes with its own set of challenges,
particularly in terms of port logistics. Efficient operations at container terminals are essential
to maintain scheduled services and minimize disruptions [
3
]. Issues, such as peak-hour
traffic and delays, require the development of systems capable of predicting and addressing
these challenges [
4
]. While some ports have implemented truck scheduling systems to
Sustainability 2025, 17, 337 https://doi.org/10.3390/su17010337
Sustainability 2025, 17, 337
2 of 26
reduce waiting times and emissions, there remains a pressing need for more comprehensive
solutions to address the logistical complexities at terminal entrances [5].
In response to these challenges, the concept of smart ports has gained momentum,
incorporating Industry 4.0 principles and digitalization to transform supply chain man-
agement. Smart port initiatives utilize advanced technologies to enable large-scale data
analysis and process automation, boosting operational efficiency and resilience [
6
]. Issa
Zadeh et al. [
7
] defines smart ports through three key sectors: intelligent logistics, intelli-
gent infrastructure, and intelligent traffic. These sectors include technologies such as IoT,
platforms, sensors, intelligent cargo handling, and energy management systems. When
these components are effectively integrated, they optimize port operations, enabling them
to meet growing demands with greater efficiency.
Despite recognizing the need for innovation, many ports still rely on manual solu-
tions that lead to inefficiencies [
6
]. Modernizing ports through digitalization is essential,
requiring the integration of systems and collaboration among stakeholders [
8
]. This trans-
formation involves creating infrastructure for real-time data collection and processing,
leveraging business intelligence tools to enhance planning and control of port opera-
tions [
9
]. Additionally, intelligent network technologies—such as IoT, information and
communication technology (ICT), and smart energy systems—play a critical role in im-
proving energy efficiency, sustainability, and the overall performance of seaports. These
technologies enable secure communication, real-time data processing, and effective energy
management, supporting the sustainable and efficient operation of smart ports [10].
International organizations, such as the United Nations Conference on Trade and
Development (UNCTAD), advocate for increased investment in digitalization to enhance
port efficiency and resilience. Furthermore, governments are urged to drive reforms in port
infrastructure and operations to facilitate the adoption of digital solutions [11].
This research aims to drive the digital and ecological transition in transport, logistics,
and port operations by introducing a decision support tool developed using Microsoft
Power BI. While Power BI itself may not be a novel technology, the innovation in this
research lies in how it is applied to integrate and analyze fragmented data from port oper-
ations, providing actionable insights that directly inform decision-making and improve
operational efficiency. By addressing challenges, such as data dispersion, enhancing logis-
tics flow analysis, and reducing port congestion, this investigation presents a compelling
case for the widespread application of business intelligence in the maritime sector, em-
phasizing its positive impact on port management and operations. For this purpose, the
application of the case took place in one of the largest container maritime ports in Portugal.
This paper is structured as follows: Section 2 reviews digital transformation in port lo-
gistics, focusing on data-driven decision-making and BI’s impact on data analysis.
Section 3
outlines the logistics processes at the terminal, highlighting container handling, service
scheduling challenges, and the need for the Power BI tool. Section 4 describes the devel-
opment methodology. Section 5 details the BI tool’s design, covering requirements, data
models, and dashboards. Section 6 evaluates the tool with a SWOT analysis. Section 7
summarizes the paper’s contribution and future research directions.
2. Literature Review
2.1. Digital Transition in Port Logistics Operations
The history of seaports can be divided into five generations, each with significant
technological advances and operational changes. Similarly, many forms of digitalization
have been implemented in ports, amounting to three generations of digitization over the
five generations of ports [1].
Sustainability 2025, 17, 337
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Before 1950, the first generation mainly involved manual port operations, such as
cargo handling and paper-based unloading procedures. Over time, these procedures
shifted to electronic formats through electronic data interchange (EDI), improving data
exchange and processing [
1
,
11
]. The second generation, until 1980, transformed ports into
centers of value-added services. This era saw increased raw material handling and the
emergence of the first port communication systems (PCS), which improved coordination
and efficiency in port operations [
11
,
12
]. Starting in 1980, the third generation saw the in-
creasing importance of intermodal transport. Information technologies aimed at enhancing
operational efficiency were introduced, with ports adopting terminal operating systems
(TOS) to integrate data from various technologies and subsystems for more cohesive and ef-
fective
management [11,13]
. The fourth generation, from 1990, brought innovation through
the integration of port companies into common administrations and the development of
automated technologies, which are crucial for improving efficiency and automation in
terminal operations [
12
,
13
]. The fifth generation, from 2010, introduced concepts such as
“Smart Ports”, “Industry 4.0”, “Digitalization”, and “Sustainability”. Ports began using
smart devices and mobile applications to optimize traffic and cargo flows. The integration
of various control centers into a centralized system aims to allow for real-time data analysis,
transforming how ports operate and adapt to modern needs [14].
Each generation brought important advancements, shaping modern ports and prepar-
ing them to meet the challenges and opportunities of the current global economy. The key
aspects of each generation of port digitalization are summarized in Table 1, illustrating the
continuous evolution and increasing complexity of port operations.
Table 1. Core elements of each port and digitalization generation, based on [12].
Port
Generation
Digitalization
Generation
Technology Purpose Contribution Obstacle
First
(Before 1960)
Second
(1960–1980)
Third
(1980–1990)
First
(1950–1990)
EDI, PCS, TOS
- Transformation
of all information
exchanges
between the
various
companies using
the port from
physical to
electronic
methods
- Better planning
of process
execution due to
the availability
of information
- Low adherence
of the port
community to
the systems
created, as
companies do
not abdicate
from using
physical
documents
Fourth
(1990–2010)
Second
(1990–2010)
RFID, TAS, Laser
- Automation of
terminal
activities
- Increased
efficiency and
operational
capacity
- Collected
information is
static
- Low adoption
of the truck
appointment
system (TAS)
Fifth
(After 2010)
Third
(since 2010)
Mobile Apps,
Cloud, Smart
devices
- Creation of
intelligent
processes that
integrate port
activity
- Better
coordination and
monitoring of
port activity
- Inefficiencies in
information flow
- A certain
reluctance
among
stakeholders to
use disruptive
processes
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2.2. Decision-Making and the Potential of Data in Seaports
Decision-making is the cognitive process of reasoning used to select an action from
among several alternatives, which is considered crucial in organizations because it has a
decisive influence on their ability to adapt to change [15].
Ports generate a variety of data types, including logistical data, operational statistics,
cargo and shipping information, as well as environmental and security data. Business
intelligence tools play a crucial role in transforming this diverse and voluminous data into
actionable insights. These tools employ processes such as extract-transform-load (ETL) and
data virtualization to integrate data from disparate sources, ensuring that the information
is accurate and accessible for analysis [
16
]. Advanced analytics and AI-driven insights,
as part of BI, enable predictive capabilities, providing foresight into future trends and
potential issues, which is essential for the dynamic environment of ports [17].
The potential of data in seaports is immense, as it supports the evolution of these
key hubs in global trade networks into intelligent, efficient, and sustainable operations.
Technologies such as the seaport data space (SDS) and big data architectures allow for
secure data sharing and improved decision-making through key performance indicators
(KPIs) displayed on dashboards. This integration can lead to decreased transaction costs
and enhanced operational quality [
18
]. Additionally, integrating port community systems
(PCS) is vital for the competitiveness of seaports, as it promotes coordinated communication
among stakeholders and boosts operational efficiency [19].
Moreover, the role of data extends beyond the seaports themselves to their intercon-
nections with inland freight distribution systems, such as dry ports. Research indicates that
operations at dry ports can impact seaport competitiveness by improving performance,
service variety, and capacity [20].
The increasing focus on data-driven decision-making in seaports is highlighted by [
21
],
who discussed the role of digital twins in facilitating transparent and controlled seaport
operations. Similarly, [22] emphasized the importance of advanced technologies and digi-
talization in optimizing seaport management. Furthermore, [
20
] underscored the impact of
dry port operations on seaport competitiveness, suggesting that seamless integration can
enhance overall performance and capacity.
Finally, [
23
] emphasized the importance of operational performance indicators and
the optimization of logistics networks for enhancing seaport efficiency.
Decision-making must be efficient and effective, and data plays a key role in providing
information to identify problems, analyze alternatives, and find solutions [24].
2.3. Business Intelligence—Benefits and Applications to Seaports
The concept of business intelligence (BI) is the discipline that integrates infrastructure
to select, extract, process, and visualize data, enabling organizations to make data-driven
decisions [25].
BI has the potential to enhance decision-making in seaports by enabling the integration
and analysis of diverse data sources. This can result in improved operational efficiency and
better strategic planning [
26
]. Through data visualization and reporting tools, shipping data,
cargo movements, and vessel tracking are presented in a clear and actionable manner [
27
].
Predictive analytics utilizes historical data to forecast cargo volumes, enabling ports to
allocate resources effectively and prepare for future demands [
28
]. Monitoring operational
performance through KPIs helps evaluate and improve the efficiency of port operations [
29
].
Furthermore, BI applications facilitate supply chain optimization by enhancing stakeholder
collaboration through effective data sharing, ultimately leading to more streamlined and
responsive supply chain management [30].
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Several authors highlight the diverse benefits of BI in the maritime sector. For in-
stance, the integration of BI with X-ray scanning technology has been proposed to improve
maritime security by providing enhanced visibility in supply chain operations [
31
]. Ad-
ditionally, the digitalization and automation of seaport infrastructures, supported by BI,
contribute to the optimization of goods and people management, as well as facilitating
a green energy transition [
22
]. Contradictions or interesting facts emerge when consider-
ing the varying stages of BI implementation across different seaports. While some ports,
like those in Croatia, are in the developmental phase, focusing on simplifying business
processes and stakeholder connectivity [
32
], others, such as those in China, have achieved
notable advancements in automation and intelligence, but still face challenges in fully
leveraging BI for business operations and decisions [33].
Digitalization is essential for the future of ports, as demonstrated by [
7
], who show
that ports with smart energy infrastructures can significantly reduce carbon emissions
and improve operational efficiency, while ports that do not adopt such solutions face
inefficiencies and high environmental [
10
]. Moreover, the implementation of business
intelligence (BI) enables ports to make quick decisions based on real-time data, optimizing
logistics flows and reducing their environmental impact. In contrast, ports that do not
adopt BI continue to operate with outdated data, resulting in operational inefficiencies
and greater environmental impacts [
10
]. Therefore, digitalization is an urgent necessity to
ensure the sustainability and competitiveness of ports in the future.
Ain et al. [
34
] highlight that BI systems offer a flexible technological solution for
accessing data from multiple sources, enabling the accumulation, integration, and analysis
of data to identify opportunities, strengths, and weaknesses. These systems support
decision-making by facilitating advanced integration and management of structured and
unstructured data, handling large volumes of data, empowering end users with enhanced
processing capabilities to derive new insights, and providing solutions for ad hoc analysis
and queries.
BI applications encompass data mining techniques, data visualization, and perfor-
mance analysis, collectively converting raw data into valuable information. This trans-
formation is achieved through the development of dashboards, which act as an interface
between the system and users, visually summarizing performance indicators [
26
]. However,
Kruglov et al. [
35
] argue that dashboard development and associated indicators should be
customized to the end user, as one of the primary reasons for the rejection of this tool is the
lack of information that users genuinely need.
I¸sık et al. [
36
] underscore the potential of BI systems to manage varying volumes of
data generated at an increasing velocity due to business activities, combined with ongoing
market changes, prompting companies to prioritize their implementation. This prioritiza-
tion is motivated by their aim to differentiate themselves from competitors through more
precise decision-making.
3. Initial Status and Challenges of the Port Under Study
In the port under study, containers are associated with either import or export flows.
For exports, the supplier dispatches the container via a hauler, and the terminal operator
manages its shipment and dispatch to the destination port. For imports, the container
arrives via maritime transport, and the terminal operator coordinates unloading and
delivery to a hauler for final customer transport.
The transportation process begins when a hauler receives a request from the exporter
or importer. To enter the terminal, both the driver and the truck must be registered and
authorized in the system. The hauler must then schedule a 1-h time window for logistical
services (delivery, pickup, or both). For example, if a delivery is scheduled from 8:00 AM
Sustainability 2025, 17, 337
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to 9:00 AM, the driver must arrive within that timeframe, and the terminal operator must
be prepared to execute the service.
Service scheduling can be done in advance using the JUL system or in person at
the terminal’s kiosk. Advance scheduling is preferred as it automatically validates the
driver’s entry authorization. In-person scheduling requires manual data entry into the
GTOS system, which integrates with JUL.
There are two systems for recording data on container services: JUL, the port’s global
information system, which centralizes all activities, and GTOS, the terminal’s operational
system, which supports planning, execution, and control. JUL tracks appointments through
various stages: “Pending” (awaiting validation), “Scheduled” (after validation), “Running”
(service in progress), and “Realized” (completed). Other statuses include “Not performed”
(service not started) and “Canceled” (due to hauler or terminal operator issues or expiry).
As container volumes increase, terminal operations face significant pressure, exposing
inefficiencies. The main issue is the disorganization caused by a lack of demand forecasting
and the terminal operator’s inability to adapt resources effectively. The JUL system’s
inefficiency, due to low hauler adoption and unexpected cancellations, leads to truck
queues and congestion at the port entrance.
Despite limited adherence to the scheduling platform, it remains useful for quantifying
scheduled services. The port authority is focused on improving processes to address these
inefficiencies and better manage the scheduling and execution of logistics services.
Technological advancements have led to the creation of isolated “data islands”, with
logistics data fragmented within JUL, resulting in integration challenges, particularly with
GTOS. This lack of data consolidation leads to inefficient information flows, which hinders
timely access to critical operational insights.
This work aims to develop a Power BI tool that extracts and transforms data from
logistics services, providing insights that support decision-making and mitigate current in-
efficiencies. The BI system will aggregate data from various sources into a single repository,
presenting relevant information in a user-friendly, graphical format to improve accessibility
and understanding across the organization.
4. Development Methodology
The development of the BI tool has followed the cross-industry standard process
for data mining methodology (Figure 1), comprising six main phases. This methodology
guarantees a systematic and effective resolution of the challenges identified, although it
is not able to fully integrate data from both systems. It is, therefore, a starting point for
obtaining agile information to support decision-making.
Sustainability 2025, 17, x FOR PEER REVIEW 7 of 28
Service scheduling can be done in advance using the JUL system or in person at the
terminal’s kiosk. Advance scheduling is preferred as it automatically validates the driver’s
entry authorization. In-person scheduling requires manual data entry into the GTOS
system, which integrates with JUL.
There are two systems for recording data on container services: JUL, the port’s global
information system, which centralizes all activities, and GTOS, the terminal’s operational
system, which supports planning, execution, and control. JUL tracks appointments
through various stages: “Pending” (awaiting validation), “Scheduled” (after validation),
“Running” (service in progress), and “Realized” (completed). Other statuses include “Not
performed” (service not started) and “Canceled (due to hauler or terminal operator
issues or expiry).
As container volumes increase, terminal operations face signicant pressure,
exposing ineciencies. The main issue is the disorganization caused by a lack of demand
forecasting and the terminal operator’s inability to adapt resources eectively. The JUL
system’s ineciency, due to low hauler adoption and unexpected cancellations, leads to
truck queues and congestion at the port entrance.
Despite limited adherence to the scheduling platform, it remains useful for
quantifying scheduled services. The port authority is focused on improving processes to
address these ineciencies and beer manage the scheduling and execution of logistics
services.
Technological advancements have led to the creation of isolated “data islands”, with
logistics data fragmented within JUL, resulting in integration challenges, particularly with
GTOS. This lack of data consolidation leads to inecient information ows, which hinders
timely access to critical operational insights.
This work aims to develop a Power BI tool that extracts and transforms data from
logistics services, providing insights that support decision-making and mitigate current
ineciencies. The BI system will aggregate data from various sources into a single
repository, presenting relevant information in a user-friendly, graphical format to
improve accessibility and understanding across the organization.
4. Development Methodology
The development of the BI tool has followed the cross-industry standard process for
data mining methodology (Figure 1), comprising six main phases. This methodology
guarantees a systematic and eective resolution of the challenges identied, although it is
not able to fully integrate data from both systems. It is, therefore, a starting point for
obtaining agile information to support decision-making.
Figure 1. Diagram of the methodology adopted based on the CRISP-DM cycle.
Figure 1. Diagram of the methodology adopted based on the CRISP-DM cycle.
Sustainability 2025, 17, 337
7 of 26
As discussed in the previous chapters, the need for this BI solution arises from the
investment in the digitalization of ports as a strategy to address the growth in maritime
transport and mitigate the lack of agile information that compromises the effectiveness of
port services. It is important to note that there are not many BI applications specifically
targeted at this sector. While the CRISP-DM methodology is not typically considered
agile, it was chosen for this study due to its structured approach, which provided a clear
and systematic framework for addressing the challenges in port operations. The dynamic
and complex nature of seaport environments requires a method that can both guide and
adapt to evolving needs. Despite its more rigid structure, CRISP-DM was implemented to
ensure a coherent process for data collection, transformation, and analysis. This approach
enabled the identification and resolution of critical data fragmentation issues, contributing
to better-informed decision-making.
During the early stages of the project, several meetings were held to understand how
the port operates. The main goal was to identify the current issues and find opportunities
to improve data analysis and decision-making procedures.
The second phase focused on identifying the key data sources for analysis. The main
sources identified were the scheduling system “JUL”, which handles appointments, and
the operational system “GTOS”, which manages operations at the terminal. A thorough
evaluation of the data’s quality was conducted to ensure completeness, accuracy, timeliness,
consistency, and accessibility. Data integrity checks were performed to ensure consistent
formatting, completeness, and the absence of null or duplicate records. Any discrepancies
found were noted for future resolution in the source systems and the BI system.
In the third phase, the focus shifted to addressing inconsistencies in the data. This
involved data cleansing and the application of data mining techniques such as classification
and regression to create additional variables. A composite key was developed to cross-
reference the data from the two systems, expanding analytical possibilities. Following
this approach, final attributes and records were selected for loading into the BI system’s
data warehouse. KPIs, which are deemed necessary by stakeholders, were established to
provide a comprehensive picture of scheduling and operational performance at the port.
Using the Kimball technique, which employs dimensional modeling to depict rela-
tionships between data tables, the fourth phase involved building the data warehouse.
Snowflake and constellation schemas were used to organize the data effectively. Subse-
quently, dashboards featuring key performance indicators were created with a visually
appealing and user-friendly interface to simplify data interpretation.
In the fifth phase, a comprehensive SWOT analysis was performed to assess the
effectiveness of the proposed solution, with a particular focus on the dashboards, since
they are the interface between the final solution and the end users. The evaluation aimed
to ascertain the tool’s ability to robustly and efficiently analyze the terminal’s systems.
The sixth and final phase involved preparing the solution for implementation to
analyze the operations of the terminal under study, which is currently under evaluation
by the port authority. A final report that included a detailed account of every phase of the
development process was also produced. The goal of this documentation is to demonstrate
the solution’s finished product and facilitate its duplication or environment adaptation,
therefore guaranteeing the solution’s ongoing improvement.
The process outputs involve delivering a functional business intelligence tool. This
technology has been actively employed at the specific port being studied to carry out a
sample analysis of the existing logistics operations and to assess its impact.
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5. BI Tool: Features and Practical Outcomes
This section outlines the key findings related to the conceptualization and development
of the business intelligence tool. The tool comprises three different modules, referred to in
this paper as applications: one for data collected from the scheduling system (referred to as
JUL), another for data collected from the operator’s system coordinating terminal activities
(referred to as GTOS), and a third for cross-referencing data from both systems (referred
to as INT). Evaluating new parameters, such as the percentage of previously scheduled
services and compliance with time windows, was only possible by cross-checking the data
in the INT application.
This modular approach was chosen to allow separate analysis of the systems, which
are not fully integrated and are operated by different entities. Developing the tool as a single
application would compromise its integrity in the event of errors related to cross-sampling
data and decrease its query performance.
5.1. Data Preparation
After importing the data from the systems, a quality study was undertaken to identify
the non-conformities in the data according to five dimensions: completeness, accuracy,
timeliness, consistency and accessibility. The solutions adopted for the non-conformities
detected are presented in Table 2 according to their purpose: cleaning; exclusion.
To simplify and guide the interpretation of the analyses, variables were created from
the initial attributes of the datasets.
The approach adopted was mainly based on constructing variables from attributes,
mainly of the datetime type, using the regression technique and, where appropriate, classi-
fying them into categories.
An example of this approach is the construction of the continuous variable “Appoint-
ment Advance” (Table 3), based on the difference between the start date of the time slot and
the creation date of the appointment, where each value was classified using the variable
“Creation Time”. For example, when the “Appointment Advance” has a negative value,
then the value shown by the “Creation Time” variable is ‘Created during the time window’.
This type of approach not only improves understanding of the values, but also makes it
easier to compare them with other attributes, enriching the scope of the analysis.
Table 3 shows the variables constructed in the JUL dataset where “Delay End” and
“End Type” follow the same approach. In order to simplify attributes that gave similar
information, they were merged into one, as is the case with “Profile_Method”, where the
union of the “Profile” and “Method” attributes was considered relevant because drivers
make exclusive use of the mobile application, while the carrier only uses the website, and
null values always correspond to the integration. One of the crucial attributes for further
analysis, the “Time Window”, was created from the respective “Start” and “End” attributes.
However, before joining these attributes, and to preserve the date provided by them, the
“Service Date” variable was created.
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Table 2. Main problems and solutions adopted in data preparation.
Dimension Problem Solution Adopted
Completeness
JUL data does not include in-person kiosk
bookings, limiting the overall view of service
flows.
Complementary analysis using GTOS data to
characterize general service flows.
GTOS data contains limited attributes to detail
services and time slots.
Integration of JUL data and creation of a
composite key to cross-reference information
between the systems.
Accuracy
Problem 1: Incorrect bookings for booking and
bill of lading (service type and container
movement). (7283 occurrences in
~80,000 records)
Cleansing: Substitution of incorrect values in
attributes, adjusting to ‘Import’ and ‘Pick-up’
as required.
Problem 2: Delivery bookings associated with
import movements.
(1 occurrence in ~80,000 records)
Cleansing: Correction of the “Container
Movement” attribute to “Export”.
Problem 3: Bookings marked as “Completed”
with cancellation reasons.
(706 occurrences in ~80,000 records)
Cleansing: Adjustment of the “Status” attribute
to ‘Cancelled’ for applicable cases.
Problem 4: Duplicate values in cancellation
reasons.
(1265 occurrences in ~80,000 records)
Cleansing: Replacement of ‘Rejected by
Terminal Operator’ with ‘Cancelled by
Terminal Operator’.
Problem 5: Times in UTC time zone (no
adjustment for Portugal’s summertime).
(All records with time data)
Cleansing: Conversion of all timestamps to
Portugal’s time zone using a custom function.
Consistency
Problem 6: Missing booking IDs in multiple
records.
Exclusion: Removal of these records
Problem 7: Missing timestamps for booking
evolution in 73% of records.
(58,650 occurrences in ~80,000 records)
Exclusion: Removal of the booking timestamp
attributes
Problem 8: Missing end-of-time-slot dates in
some records.
(25 occurrences in ~80,000 records)
Exclusion: Removal of these records
Problem 9: Delivery and pick-up bookings for
the same container with inconsistent statuses.
(9 occurrences in ~80,000 records)
Exclusion: Removal of these records
Problem 10: Inconsistent formats in truck and
container license plates.
Cleansing: Removal of non-alphanumeric
characters from the respective attributes using
a custom function
Problem 11: Inconsistent formats in transport
company names.
Cleansing: Manual mapping to align names
between JUL and GTOS; development of an
auxiliary function for standardization.
Timeliness
The period of the JUL dataset is between November 2021 and February 2024, and the GTOS
dataset is between January 2022 and December 2023, so the data are up to date in time, where the
ease of future updates is related to the use of the same SQL query and its accessibility.
Accessibility
The process of extracting the data was carried out using an SQL query by specialized technicians
from the respective systems. The query generated Excel files that served as the starting point for
building the proposed BI tool. Therefore, it is not possible to assess the accessibility of the data,
and it is important to note that future updates of the tool and the dataset will require the use of the
same SQL query or, alternatively, adjustments based on the logic explained throughout the work.
GTOS
(specific)
Problem 12: Outliers in service duration.
Implementation of mechanisms to detect and
exclude outliers, while allowing users the
option to manually analyze them.
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Table 3. Variables created in the JUL dataset.
Variable Name Description Type
Appointment Advance
Difference in minutes between the start of the scheduled time window
and the creation time.
Double
Creation Time
Classification of Appointment Advance: ‘Before Window’, ‘During
Window’, ‘During Integration’, or ‘After Window’.
String
Profile_Method
Combination of “Profile” and “Method”: ‘Web-Transporter’,
‘App-Driver’, ‘Integration’.
String
Service Date Date for which the service was scheduled. Date
Time Window
Concatenation of the start and end times of the time window in a
simplified format, e.g., ‘16:00–17:00’.
String
Delay End
Difference in minutes between the recorded completion timestamp
and the end of the time window.
Double
End Type
Classification of how the scheduling was completed: ‘Not Recorded’,
‘Cancelled’, ‘Auto Closed’, ‘Late Close’, ‘On-Time Close’.
String
Table 4 shows the variables created in the GTOS dataset. Initially, due to the lack of
a unique identifier for each service, the “Service ID” was introduced. Since there was no
attribute in this dataset that described the “Type of Service” according to the specification
presented, this variable was constructed by classifying combinations of the attributes
“Loads”, “Unloads”, and “Type of Journey”. For example, a double trip corresponds to a
delivery and lifting service, while if it is a single trip and there is a record of an unloaded
container, it is a delivery, and the other possible case corresponds to the lifting of a container.
To analyze the duration of the services and gauge the performance of the terminal operator
in completing them, the variable “Service Duration” was constructed from the difference
between the time the truck left and entered the terminal. Considering the lack of context
about the time windows marked in this dataset, the variables “Entry Time Window” and
“Exit Time Window” were created to group truck entries and exits into one-hour intervals
throughout the day, as stipulated in the terminal operator’s schedule. Before creating
these variables and to preserve the date present in them, the “Service Date” was entered.
Ideally, a service should be completed within the same time window in which the truck
entered the terminal. To assess this compliance, the “Time Offset” variable was introduced,
which represents the difference, in hours, between the truck’s departure and entry time.
Therefore, this metric quantifies the number of time windows that elapsed between the
truck’s departure and entry of the truck at the terminal.
Table 4. Variables created in the GTOS dataset.
Variable Name Description Type
Service ID Unique identifier for each service. Integer
Service Date Datetime when the truck entered the terminal for the service. Date
Service Type Classification of the service: ‘Delivery’, ‘Pickup’, or ‘Delivery and Pickup’. String
Service Duration Duration in minutes from the truck’s terminal entry to its exit. Double
Entry Time Window 1-h time slot during which the truck entered the terminal. String
Exit Time Window 1-h time slot during which the truck exited the terminal. String
Time Offset Difference in hours between the time windows of truck exit and entry. Integer
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Mechanisms for identifying outliers were also added. Based on the interquartile range
(IQR) technique, a method was developed to detect univariate outliers, shown in Figure 2
with the example of the variable “Average Service Duration”, which corresponds to GTOS
problem 12 identified in the data quality survey (Table 2, last row). The IQR method,
intrinsic to the box-and-whisker plot, was chosen for this work as it is a simple, robust, and
widely used technique from a general practical perspective, with no specific assumptions
about the data distribution. Outliers identified by the method are excluded by default, but
users have the option to analyze them further if required. However, this method can be
applied in an equivalent way to other variables, depending on requirements.
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Figure 2. Outlier identication method.
Table 5 summarizes the evolution of the aributes during data processing. Data
preparation resulted in the exclusion of 5663 records in JUL and the standardization of the
aributes in both datasets. In addition, seven variables were created in JUL and seven in
GTOS, which will play a crucial role in the analyses. Finally, 16 aributes in JUL and 3 in
GTOS that did not add value were excluded. Thus, 19 aributes were selected for the nal
JUL model and 11 aributes for the nal GTOS model.
Table 5. Evolution of the number of aributes and records throughout data processing.
System
No. Initial
Aributes
No. Initial
Records
No. Final
Aributes
No. Final
Records
JUL 27 100.348 19 94.685
GTOS 7 111.984 11 111.984
5.2. Requirements and Indicators for the BI Tool
The requirements were raised with the stakeholders, considering the context of the
seaport under study and the data available for collection from existing systems. The
iterative process to dene the nal key performance indicators (KPIs) involved multiple
stages. Initially, a comprehensive set of potential KPIs was identied, based on the raw
data extracted from the operational systems. This preliminary list was evaluated in a
series of meetings with stakeholders, where the relevance and applicability of each KPI
were discussed in detail.
These discussions revealed that certain indicators were less critical or redundant and
were subsequently excluded. Conversely, priority was given to the indicators that directly
addressed identied monitoring needs, such as operational adjustments, resource
optimization, and strategic decision-making. Ultimately, 14 KPIs were selected from the
initial set, as they most eectively reected the specic requirements of each user prole.
The relevant user proles have been identied (“terminal operators”, “port
administrations”, “haulers and drivers”) with dedicated dashboards. Dashboards were
implemented for haulers and their drivers to display the logistics operator’s capacity
availability for various time windows and days of the week. Terminal operators needed
dashboards to showcase the most requested containers, periods of highest demand, and
the haulers with the highest number of appointments, to adjust their resources and
prevent service disruptions. For port administrations, the dashboards were required to
present summary performance indicators such as the percentage of prior appointments
and the time in advance they were conducted. In the laer case, the analysis provided by
the dashboards is aimed to support the implementation of strategic port policies (for
example, the implementation of mandatory prior scheduling).
Figure 2. Outlier identification method.
Table 5 summarizes the evolution of the attributes during data processing. Data
preparation resulted in the exclusion of 5663 records in JUL and the standardization of the
attributes in both datasets. In addition, seven variables were created in JUL and seven in
GTOS, which will play a crucial role in the analyses. Finally, 16 attributes in JUL and 3 in
GTOS that did not add value were excluded. Thus, 19 attributes were selected for the final
JUL model and 11 attributes for the final GTOS model.
Table 5. Evolution of the number of attributes and records throughout data processing.
System
No. Initial
Attributes
No. Initial
Records
No. Final
Attributes
No. Final
Records
JUL 27 100.348 19 94.685
GTOS 7 111.984 11 111.984
5.2. Requirements and Indicators for the BI Tool
The requirements were raised with the stakeholders, considering the context of the
seaport under study and the data available for collection from existing systems. The
iterative process to define the final key performance indicators (KPIs) involved multiple
stages. Initially, a comprehensive set of potential KPIs was identified, based on the raw
data extracted from the operational systems. This preliminary list was evaluated in a series
of meetings with stakeholders, where the relevance and applicability of each KPI were
discussed in detail.
These discussions revealed that certain indicators were less critical or redundant
and were subsequently excluded. Conversely, priority was given to the indicators that
directly addressed identified monitoring needs, such as operational adjustments, resource
optimization, and strategic decision-making. Ultimately, 14 KPIs were selected from the
initial set, as they most effectively reflected the specific requirements of each user profile.
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The relevant user profiles have been identified (“terminal operators”, “port administra-
tions”, “haulers and drivers”) with dedicated dashboards. Dashboards were implemented
for haulers and their drivers to display the logistics operator’s capacity availability for
various time windows and days of the week. Terminal operators needed dashboards
to showcase the most requested containers, periods of highest demand, and the haulers
with the highest number of appointments, to adjust their resources and prevent service
disruptions. For port administrations, the dashboards were required to present summary
performance indicators such as the percentage of prior appointments and the time in ad-
vance they were conducted. In the latter case, the analysis provided by the dashboards is
aimed to support the implementation of strategic port policies (for example, the implemen-
tation of mandatory prior scheduling).
A consistent requirement across all user profiles was the need for the tool to be
user-friendly, with information presented clearly and concisely using appropriate graphs
and diagrams. The selected indicators were also designed to be analyzed from three
perspectives: daily trends, overall performance, and contribution to the total, offering
flexibility in monitoring operations.
Out of 30 final attributes selected in the quality treatment (the sum of the fourth
column values, Table 5), 14 KPIs were established and implemented using Power BI’s data
analysis expressions (DAX) language (Table 6). The indicators can be filtered by context,
enabling diversified analysis without modifying their structure.
Table 6. KPIs defined and their description.
KPI Description Application
No. Appointments Unique count of prior appointments in the JUL application.
JUL
% Cancellation
Percentage of canceled prior appointments, calculated by
dividing the number of canceled prior appointments by the
total number of prior appointments.
Average Appointment Advance
Average time (minutes) between appointment scheduling time
and the start of the scheduled time window.
Containers per Appointment
Total number of containers handled by each prior
appointment.
No. Services Unique count of services in the GTOS application.
GTOS
Average Duration of Service Average time (minutes) taken per service.
% Entry and Exit Same Time Window
Ratio between the number of trucks entering and leaving in
the same time window and the total number of trucks that
entered in the same time window.
Availability Time Window
Difference between the operator’s capacity and the number of
services per time window divided by the total number of
services.
Containers Delivered and Picked Up Total number of containers handled per service.
% Scheduled Services
Percentage of services scheduled in advance obtained by
dividing the number of previously scheduled services by the
total number of services.
INT
No. Scheduled Services Unique count of scheduled services in the INT application.
% Compliance
Percentage of services completed within the scheduled time
window obtained through the ratio between the number of
services that comply the time window and the total number of
services.
Average Entry Delay
Average time (minutes) between the start of the time window
and the time the truck entered the terminal
Average Exit Delay
Average time (minutes) between the end of the time window
and the time the truck exited the terminal
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The “No. Appointments” is the KPI for assessing the JUL’s performance. By quanti-
fying previous appointments, it is possible to monitor variations and identify trends that
allow for adjustments in strategy during peak demand periods. Additionally, knowing the
number of prior appointments is essential for measuring the adoption of this system by
haulers and their drivers, which is crucial for quantifying the current problem.
The “% Cancellation” KPI is a measure of the proportion of previous appointments
that have been canceled compared to the total number of prior appointments. This metric
is significant as it highlights the effectiveness and reliability of JUL. A high cancellation
rate can indicate various issues. Difficult-to-use systems might lead to scheduling errors,
causing terminal operators to cancel. Additionally, a lack of flexibility could force haulers
to cancel. Poorly adjusted operational capacity might also prevent terminal operators from
meeting demand, leading to dissatisfaction among haulers. Therefore, maintaining a low
cancellation rate is essential for ensuring smooth operations and high hauler satisfaction.
The “Average Appointment Advance” KPI gives the average time between the creation
of the appointment and the start time window of the service. Monitoring these data is
essential to avoid problems with operational capacity and flexibility. A shorter average
time in advance may signal imminent service overload, leading to challenging resource
management and delays. Conversely, a longer average time in advance enables more
efficient operational planning, driving productivity gains.
The “Containers per Appointment” KPI shows the number of containers handled
in each previous appointment, which helps in making more accurate adjustments in the
allocation of resources. Also, the terminal operator’s income depends on the movement
of containers, so understanding the number of containers moved is crucial for managing
operating costs and income effectively.
The “No. of Services” is a metric that captures the total count of services registered in
GTOS. Unlike the “No. of Appointments”, which records previously scheduled appoint-
ments, this indicator quantifies the actual provision of services at the port. It plays a crucial
role in identifying potential expansion opportunities or the need to adjust service types.
By analyzing this metric, terminal operators can identify areas with a growing demand
for specific services, enabling them to make informed investment decisions to better meet
customer needs and gain a larger market share.
The “Service Duration” KPI refers to the average time spent by the terminal operator
on each service. This measure is valuable for identifying variations in the level of service
provided to different haulers and/or at different times. Analyzing these data make it
possible to plan the number of services that can be carried out within a specific timeframe,
ensuring optimal and effective capacity utilization.
The “%Entry and Exit Same Time Window” KPI empowers terminal operators to
evaluate the efficiency of services using only the data from its system (GTOS), and it is
calculated as the ratio between the number of trucks entering and leaving in the same
time window and the total number of trucks that entered in the same time window. A
high value for this indicator implies effective coordination across different stages of the
logistics process, which is essential for ensuring seamless operations. On the other hand, a
low value may signal planning issues that result in delays, congestion, and dissatisfaction
among haulers.
The “Availability Time Window” KPI assesses the unused capacity in each period
through the difference between the operator’s capacity and the number of services per time
window divided by the total number of services. This indicator is essential for improving
resource utilization and planning. By identifying periods of idle capacity, the terminal can
reallocate resources, adjust the scheduling of operations to fill these periods, and reduce
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costs associated with underutilization. At the same time, better management of available
capacity makes it possible to increase operational efficiency.
The “Containers Delivered and Picked Up” KPI counts all the containers handled at
the port. This metric provides a comprehensive count of the containers handled, unlike
the “Containers per Appointment” indicator, which only counts containers from previous
appointments. This helps in estimating the total operating costs and revenues related to
container handling, which is crucial for ensuring the profitability of the business.
The “% Scheduled Services” is a KPI calculated by dividing the “No. of Appointments”
by the “No. of Services”, reflecting the percentage of services scheduled in advance at JUL.
The optimal target for this metric is close to 100 percent. Lower values may indicate a lack of
prior scheduling, while higher values could suggest missed appointments. Analyzing this
metric provides insight into hauler adoption of the scheduling system and helps evaluate
excessive demand or underutilization of resources.
The “No. of Scheduled Services” quantifies the total number of records in the third
application (INT). Like the “No. of Services” and “No. of Appointments”, this indicator is
vital for assessing other indicators within this application.
The “%Compliance” KPI quantifies the percentage of services that are fulfilled within
the scheduled time window, and is essential for assessing the haulers’ commitment to
comply with the agreed time window and the terminal operator’s ability to provide the
service as planned. High values for this indicator show that the services are fulfilled by
those involved within the established schedule, improving operational efficiency and effec-
tiveness. Therefore, this metric measures the punctuality of services, driving both internal
efficiency and hauler satisfaction, which is essential for the growth and sustainability of
the business.
The “Average Entry Delay” refers to the time difference between when the truck enters
the terminal and the start time of the time window. The “Average Exit Delay” refers to
the time difference between when the truck leaves the terminal and the end time of the
time window. Monitoring and reducing these delays contribute to hauler satisfaction by
providing a more predictable and reliable service.
The establishment of these requirements and the subsequent processing of the data
turned raw data into actionable information, as the indicators established make it possible
to measure the operational performance of seaports.
5.3. The BI Tool from the Perspective of Data Architecture and Technology
Regarding the architecture of the data model, the Kimball method was used due
to its suitability for the context and requirements identified. This method was the most
appropriate considering the data available, the lower complexity of the model, and the
shorter development time. Furthermore, this design offers a straightforward solution with
quick response times, increasing the tool’s usability for end users.
In the applications for the separate systems (Figure 3), the data model was constructed
in a uniform manner. It is important to note that only the date dimension (‘dCalendar’) was
normalized, as an analysis of the fact tables (‘fAppointments’, left of Figure 3 ‘fServices’,
right of Figure 3) revealed that other dimensions would typically have just one or two
attributes. This decision was based on prioritizing the performance of Power BI, which
can effectively handle simpler data models, thus avoiding unnecessary complexity from
adding dimensions with minimal attributes. The emphasis was placed on practicality and
efficiency in data analysis, maintaining a straightforward and easily interpretable model.
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appointments. This helps in estimating the total operating costs and revenues related to
container handling, which is crucial for ensuring the protability of the business.
The “% Scheduled Services” is a KPI calculated by dividing the “No. of
Appointments” by the “No. of Services”, reecting the percentage of services scheduled
in advance at JUL. The optimal target for this metric is close to 100 percent. Lower values
may indicate a lack of prior scheduling, while higher values could suggest missed
appointments. Analyzing this metric provides insight into hauler adoption of the
scheduling system and helps evaluate excessive demand or underutilization of resources.
The “No. of Scheduled Services” quanties the total number of records in the third
application (INT). Like the “No. of Services” and “No. of Appointments”, this indicator is
vital for assessing other indicators within this application.
The “%Compliance” KPI quanties the percentage of services that are fullled within
the scheduled time window, and is essential for assessing the haulers’ commitment to
comply with the agreed time window and the terminal operator’s ability to provide the
service as planned. High values for this indicator show that the services are fullled by
those involved within the established schedule, improving operational eciency and
eectiveness. Therefore, this metric measures the punctuality of services, driving both
internal eciency and hauler satisfaction, which is essential for the growth and
sustainability of the business.
The “Average Entry Delay” refers to the time dierence between when the truck
enters the terminal and the start time of the time window. The “Average Exit Delay” refers
to the time dierence between when the truck leaves the terminal and the end time of the
time window. Monitoring and reducing these delays contribute to hauler satisfaction by
providing a more predictable and reliable service.
The establishment of these requirements and the subsequent processing of the data
turned raw data into actionable information, as the indicators established make it possible
to measure the operational performance of seaports.
5.3. The BI Tool from the Perspective of Data Architecture and Technology
Regarding the architecture of the data model, the Kimball method was used due to
its suitability for the context and requirements identied. This method was the most
appropriate considering the data available, the lower complexity of the model, and the
shorter development time. Furthermore, this design oers a straightforward solution with
quick response times, increasing the tool’s usability for end users.
Figure 3. Data of the JUL application (left) and GTOS application (right).
In both models (Figure 3), the tables are linked using the “Date” aribute, which
serves as the primary key of the ‘dCalendar’ table. This means that an appointment or
service occurs exclusively on a date, which may correspond to a holiday at most,
indicating a snowake schema. The fact that the ‘subdHoliday’ table is a subdimension of
the ‘dCalendar’ table further conrms this schema.
Figure 3. Data of the JUL application (left) and GTOS application (right).
In both models (Figure 3), the tables are linked using the “Date” attribute, which
serves as the primary key of the ‘dCalendar table. This means that an appointment or
service occurs exclusively on a date, which may correspond to a holiday at most, indicating
a snowflake schema. The fact that the ‘subdHoliday’ table is a subdimension of the
‘dCalendar’ table further confirms this schema.
The third application (INT) included the ‘dWindow’, ‘dTypeService’, and ‘dRoad-
Hauler dimensions. Each of these dimensions has a single attribute that acts as a primary
key: “Window”, “Type_Service”, and “Name_RH” (Figure 4). Despite the initial assump-
tion of avoiding the creation of dimensions with only one attribute, in this specific case,
this approach was followed given the presence of multiple fact tables and the need to filter
them simultaneously (‘fAppointments’ and ‘fServices’) to evaluate indicators such as “%
Scheduled Services”.
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The third application (INT) included the ‘dWindow’, ‘dTypeService’, and
‘dRoadHauler’ dimensions. Each of these dimensions has a single aribute that acts as a
primary key: “Window”, “Type_Service”, and “Name_RH” (Figure 4). Despite the initial
assumption of avoiding the creation of dimensions with only one aribute, in this specic
case, this approach was followed given the presence of multiple fact tables and the need
to lter them simultaneously (‘fAppointments’ and ‘fServices’) to evaluate indicators such
as “% Scheduled Services”.
Figure 4. Data model of the INT application.
Thus, there are three fact tables in the model, each ltered by dierent dimensions,
resulting in a constellation schema (Figure 4). The appointment (‘fAppointments’) and
service (‘fServices’) are each associated with only one type of service, hauler, time
window, and date. Additionally, the ‘fIntegration’ table contains records of where
appointments from ‘fAppointments’ and services from ‘fServices’ were matched. This
table is ltered only by “Date”, following the approach used in the previous models
(Figure 3).
This modeling approach ensured eective and ecient data analysis, while
maintaining the simplicity and robustness of Power BI’s performance.
5.4. The BI Tool from the Perspective of Interaction with Users
The dashboards serve as the core deliverable of the BI tool and act as the primary
interface for engaging with end users. Customized dashboards were developed for each
application to meet the specic needs of the identied user proles. Additionally, each
application features a starting menu designed to enable smooth navigation through the
range of dashboards, as illustrated in Figure 5.
Figure 4. Data model of the INT application.
Thus, there are three fact tables in the model, each filtered by different dimensions,
resulting in a constellation schema (Figure 4). The appointment (‘fAppointments’) and
service (‘fServices’) are each associated with only one type of service, hauler, time window,
and date. Additionally, the ‘fIntegration’ table contains records of where appointments
from ‘fAppointments’ and services from ‘fServices’ were matched. This table is filtered
only by “Date”, following the approach used in the previous models (Figure 3).
This modeling approach ensured effective and efficient data analysis, while maintain-
ing the simplicity and robustness of Power BI’s performance.
5.4. The BI Tool from the Perspective of Interaction with Users
The dashboards serve as the core deliverable of the BI tool and act as the primary
interface for engaging with end users. Customized dashboards were developed for each
application to meet the specific needs of the identified user profiles. Additionally, each
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application features a starting menu designed to enable smooth navigation through the
range of dashboards, as illustrated in Figure 5.
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Figure 5. Menu of the three applications (JUL top left, GTOS top right and INT boom).
In terms of structure and presentation, there are shared views and elements among
the dierent applications, making it easier to use the tool across various systems for
parallel use and comparison of appointment volumes and services.
The applications share the following three views (Figure 5). The categories view is
helpful for terminal operator analysis because it allows comparison of the system’s
primary indicators across all categories. The time view supports the terminal operator in
spoing paerns and foreseeing potential issues by making it simpler to comprehend how
indications change over time. The summary view (available only in JUL and GTOS
analytics) provides a high-level overview that enables the port authority to eciently
assess the terminal’s performance by summarizing the KPIs of the system.
Additionally, there are specic dashboards in each system, adapted to their
particularities: in JUL, there is a dashboard that covers only cancellations; in GTOS, there
is a dashboard that presents the availability of time windows; and in INT, a dashboard
that assesses compliance with the time window.
Several dashboards were developed, each prominently displaying the identication
of the source application and the specic object being analyzed, providing a clear and
organized interface for users to make comparisons and evaluations. The ve most relevant
dashboards, out of a total of ten developed, will be presented next.
The “Categories View” dashboard aims to provide a detailed and versatile analysis
of appointment data across dierent categories. This dashboard is illustrated when used
in JUL analytics (Figure 6), and displays two indicators based on the selections made in
the left menus. The top graph shows the “Average Appointment Advance”, based on the
category chosen in the top left selection menu (this top graph also provides a color
gradient representation of the Number of Appointments). The boom graph shows the
“Number of Appointments”, again based on the category chosen in the top left selection
menu and on the selections made in the other two menus. The boom graph aims to
provide a comprehensive view of the distribution of the number of appointments across
dierent values of the chosen category. These data can be further analyzed daily, as a
total, or as a percentage of the total, giving users versatile options for assessing and
understanding the appointment data.
This dashboard is important as it allows the prole of the terminal operator to explore
the appointment data across various categories, uncovering unique trends and insights
specic to each category. By doing so, it helps in making more informed and targeted
Figure 5. Menu of the three applications (JUL top left, GTOS top right and INT bottom).
In terms of structure and presentation, there are shared views and elements among the
different applications, making it easier to use the tool across various systems for parallel
use and comparison of appointment volumes and services.
The applications share the following three views (Figure 5). The categories view
is helpful for terminal operator analysis because it allows comparison of the system’s
primary indicators across all categories. The time view supports the terminal operator
in spotting patterns and foreseeing potential issues by making it simpler to comprehend
how indications change over time. The summary view (available only in JUL and GTOS
analytics) provides a high-level overview that enables the port authority to efficiently assess
the terminal’s performance by summarizing the KPIs of the system.
Additionally, there are specific dashboards in each system, adapted to their partic-
ularities: in JUL, there is a dashboard that covers only cancellations; in GTOS, there is a
dashboard that presents the availability of time windows; and in INT, a dashboard that
assesses compliance with the time window.
Several dashboards were developed, each prominently displaying the identification
of the source application and the specific object being analyzed, providing a clear and
organized interface for users to make comparisons and evaluations. The five most relevant
dashboards, out of a total of ten developed, will be presented next.
The “Categories View” dashboard aims to provide a detailed and versatile analysis
of appointment data across different categories. This dashboard is illustrated when used
in JUL analytics (Figure 6), and displays two indicators based on the selections made in
the left menus. The top graph shows the “Average Appointment Advance”, based on the
category chosen in the top left selection menu (this top graph also provides a color gradient
representation of the Number of Appointments). The bottom graph shows the “Number
of Appointments”, again based on the category chosen in the top left selection menu and
on the selections made in the other two menus. The bottom graph aims to provide a
comprehensive view of the distribution of the number of appointments across different
values of the chosen category. These data can be further analyzed daily, as a total, or as a
percentage of the total, giving users versatile options for assessing and understanding the
appointment data.
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decision-making processes, enabling users to make data-driven choices tailored to the
specic trends and characteristics identied within each category.
In the example of Figure 6, the analysis category is the year (marked in orange). The
upper graph shows that the time in advance of appointments has increased over the years,
but 31.9 min is still a low value, leaving limited exibility for the terminal operator. The
year with the highest number of appointments is 2022, indicated by the darkest bar in the
upper graph and shown in the lower graph. In the caption of the lower graph, the category
is the type of service (marked in green), and the indicator is on a total basis (marked in
blue). Delivery is the type of service with the highest number of appointments, although
it dropped signicantly from 2022 to 2023.
The only dierences between this type of dashboard and the other applications are
the indicators analyzed. In the GTOS application, there are the “Average Duration of
Service”, “% Entry and Exit Same Time Window”, and “No. Services”. In the INT
application, there are the “% Scheduled Services”, “No. Appointments”, and “No.
Services”.
Figure 6. Categories view dashboard—JUL analytics.
The “Summary View” dashboard aims to oer a comprehensive overview of the
main performance indicators for the system being analyzed. This dashboard, when
viewed in GTOS analytics (Figure 7), provides information on the daily number of
services (outlined in blue). These services are categorized by type of service and grouped
into containers delivered and picked up per day, allowing for a deep understanding of
the various service activities. This level of detail is crucial for stakeholders, especially the
port authority prole, as it enables them to closely monitor the system’s performance and
make well-informed decisions based on the most accurate and up-to-date data available.
Based on Figure 7’s example, there were, on average, 204 services provided each day,
with delivery and pick-up services accounting for the majority (109), delivery-only
services coming in second with 51 services per day, and pick-up-only services coming in
last with 44 services. This gure shows that 164 containers were delivered and 174 were
collected daily on average. Furthermore, just 31% of the trucks departed within the same
time window that they arrived, and each service took an average of 58 min.
In the JUL application, this dashboard shows the “No. Appointments”, “ Containers
per Appointment”, “% Cancellation”, and “Average Appointment Advance”.
Figure 6. Categories view dashboard—JUL analytics.
This dashboard is important as it allows the profile of the terminal operator to explore
the appointment data across various categories, uncovering unique trends and insights
specific to each category. By doing so, it helps in making more informed and targeted
decision-making processes, enabling users to make data-driven choices tailored to the
specific trends and characteristics identified within each category.
In the example of Figure 6, the analysis category is the year (marked in orange). The
upper graph shows that the time in advance of appointments has increased over the years,
but 31.9 min is still a low value, leaving limited flexibility for the terminal operator. The
year with the highest number of appointments is 2022, indicated by the darkest bar in the
upper graph and shown in the lower graph. In the caption of the lower graph, the category
is the type of service (marked in green), and the indicator is on a total basis (marked in
blue). Delivery is the type of service with the highest number of appointments, although it
dropped significantly from 2022 to 2023.
The only differences between this type of dashboard and the other applications are the
indicators analyzed. In the GTOS application, there are the “Average Duration of Service”,
“% Entry and Exit Same Time Window”, and “No. Services”. In the INT application, there
are the “% Scheduled Services”, “No. Appointments”, and “No. Services”.
The “Summary View” dashboard aims to offer a comprehensive overview of the main
performance indicators for the system being analyzed. This dashboard, when viewed in
GTOS analytics (Figure 7), provides information on the daily number of services (outlined
in blue). These services are categorized by type of service and grouped into containers
delivered and picked up per day, allowing for a deep understanding of the various service
activities. This level of detail is crucial for stakeholders, especially the port authority profile,
as it enables them to closely monitor the system’s performance and make well-informed
decisions based on the most accurate and up-to-date data available.
Based on Figure 7’s example, there were, on average, 204 services provided each day,
with delivery and pick-up services accounting for the majority (109), delivery-only services
coming in second with 51 services per day, and pick-up-only services coming in last with
44 services. This figure shows that 164 containers were delivered and 174 were collected
daily on average. Furthermore, just 31% of the trucks departed within the same time
window that they arrived, and each service took an average of 58 min.
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Sustainability 2025, 17, x FOR PEER REVIEW 19 of 28
Figure 7. Summary view dashboard—GTOS analytics.
The “Availability” dashboard (Figure 8) seeks to give terminal operators and haulers
a clear and actionable picture of time window availability throughout the week. Using
color coding, the dashboard eectively shows terminal utilization. Red indicates higher
utilization, while green denotes lower utilization. This intuitive design oers valuable
insights, and supports terminal operators in eciently managing resource allocation.
Moreover, the dashboard helps haulers make well-informed decisions about their
movements, reducing unnecessary congestion at the terminal during peak times. By
utilizing these data, the terminal operators and haulers proles can optimize their
processes and improve overall operational eciency.
In the example presented in Figure 8, the response capacity of the operator stands at
30 services per hour (displayed in blue). In this example, it becomes clear that the time
windows of highest demand during the analyzed week (highlighted in orange) were 8:00
AM–9:00 AM and 4:00 PM–5:00 PM (it is worth noting that the developed tool excludes
public holidays from this analysis).
Figure 7. Summary view dashboard—GTOS analytics.
In the JUL application, this dashboard shows the “No. Appointments”, “ Containers
per Appointment”, “% Cancellation”, and “Average Appointment Advance”.
The “Availability” dashboard (Figure 8) seeks to give terminal operators and haulers a
clear and actionable picture of time window availability throughout the week. Using color
coding, the dashboard effectively shows terminal utilization. Red indicates higher utiliza-
tion, while green denotes lower utilization. This intuitive design offers valuable insights,
and supports terminal operators in efficiently managing resource allocation. Moreover, the
dashboard helps haulers make well-informed decisions about their movements, reducing
unnecessary congestion at the terminal during peak times. By utilizing these data, the
terminal operators and haulers profiles can optimize their processes and improve overall
operational efficiency.
Sustainability 2025, 17, x FOR PEER REVIEW 20 of 28
Figure 8. Availability dashboard—GTOS analytics.
The “Time View” dashboard aims to provide a wide-ranging analysis of how system
indicators change over time. It helps to identify paerns, trends, and seasonality in the
data, enabling beer insight into the temporal evolution of these indicators. The one
present in INT analytics (Figure 9) boasts two primary graphs designed to provide an in-
depth understanding of the data. The upper graph shows the indicator selected in the
middle menu over the period chosen in the left menu, allowing users to gain valuable
insights into performance paerns over time. Meanwhile, the lower graph oers a
comparative view of the indicator’s variation, allowing for the detailed observation of
changes on a monthly or yearly basis. This comparison is crucial for the terminal
operator’s prole, as it enables the identication of seasonal paerns, detection of
anomalies, and evaluation of the eectiveness of operational strategies across various time
frames. Armed with these insights, this prole can proactively plan for future demands
and challenges, as well as craft eective responses to the prevailing trends in the data.
In the example shown in Figure 9, the year 2023 was selected (highlighted in orange),
and it was immediately apparent that the annual “%Scheduled Services” was 47%, after
which the indicator “%Scheduled Services” was chosen (highlighted in black). Recall that
the “%Scheduled Services” KPI indicates the percentage of services scheduled in advance
at JUL. In the bar graph, the captions showing this indicator were selected, both for the
year picked (2023, in light blue) and for the previous year (2022, in dark blue), and in the
line graph, the variation compared to the previous year was selected (highlighted in red).
In this example, the upper graph shows that, in all months of 2023, the proportion of
appointments dropped, except in April, when there was a slight increase (0.6%) compared
to the same month in 2022. The graph below corroborates this trend by quantifying the
decreases, which were signicant in September, October, and December, reaching
reductions of around 30%.
In the JUL application, this dashboard only presents the “No. of appointments”,
although there are more lter categories.
Figure 8. Availability dashboard—GTOS analytics.
In the example presented in Figure 8, the response capacity of the operator stands
at 30 services per hour (displayed in blue). In this example, it becomes clear that the
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time windows of highest demand during the analyzed week (highlighted in orange) were
8:00 AM–9:00 AM and 4:00 PM–5:00 PM (it is worth noting that the developed tool excludes
public holidays from this analysis).
The “Time View” dashboard aims to provide a wide-ranging analysis of how system
indicators change over time. It helps to identify patterns, trends, and seasonality in the data,
enabling better insight into the temporal evolution of these indicators. The one present
in INT analytics (Figure 9) boasts two primary graphs designed to provide an in-depth
understanding of the data. The upper graph shows the indicator selected in the middle
menu over the period chosen in the left menu, allowing users to gain valuable insights
into performance patterns over time. Meanwhile, the lower graph offers a comparative
view of the indicator’s variation, allowing for the detailed observation of changes on a
monthly or yearly basis. This comparison is crucial for the terminal operator’s profile, as it
enables the identification of seasonal patterns, detection of anomalies, and evaluation of
the effectiveness of operational strategies across various time frames. Armed with these
insights, this profile can proactively plan for future demands and challenges, as well as
craft effective responses to the prevailing trends in the data.
Figure 9. Time view dashboard—INT analytics.
In the example shown in Figure 9, the year 2023 was selected (highlighted in orange),
and it was immediately apparent that the annual “%Scheduled Services” was 47%, after
which the indicator “%Scheduled Services” was chosen (highlighted in black). Recall that
the “%Scheduled Services” KPI indicates the percentage of services scheduled in advance
at JUL. In the bar graph, the captions showing this indicator were selected, both for the
year picked (2023, in light blue) and for the previous year (2022, in dark blue), and in the
line graph, the variation compared to the previous year was selected (highlighted in red).
In this example, the upper graph shows that, in all months of 2023, the proportion of
appointments dropped, except in April, when there was a slight increase (0.6%) compared
to the same month in 2022. The graph below corroborates this trend by quantifying
the decreases, which were significant in September, October, and December, reaching
reductions of around 30%.
In the JUL application, this dashboard only presents the “No. of appointments”,
although there are more filter categories.
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The purpose of the “Compliance” dashboard is to analyze the terminal operator’s
capacity to carry out services according to the schedule and to conduct a study of how
effectively haulers and their drivers complied with the scheduled time windows when
they had previously scheduled them. It is a sophisticated dashboard, available only in
the INT application, which extracts data from various systems and allows the terminal
operator profile to carry out an exhaustive analysis of activities. The dashboard consists of
four graph components, each offering a unique and detailed analytical perspective. Users
can choose specific indicators and categories to conduct a comprehensive examination.
The graph in the top left visually displays the values of the time indicator across different
elements within the selected category on the left menu. Meanwhile, the bottom left graph
provides the “%Compliance” for these category elements, giving a detailed understanding
of adherence levels. Additionally, the top right graph enables exploration of the relationship
between category values and the number of scheduled services. Lastly, the bottom right
graph visually depicts the distribution of services based on time window compliance, along
with interactive filtering options that work seamlessly with selections made in the other
graphs, allowing for a comprehensive and detailed analysis. This dashboard showcases the
successful integration of various datasets, enabling innovative analysis of terminal data.
While creating the “fIntegration” table was complex and had limitations (Figure 4), it was
crucial for measuring previously inaccessible indicators, such as “%Compliance”.
In Figure 10, the chosen category represents the type of service (highlighted in or-
ange). It’s evident that the delivery and pick-up service, despite being the most common
(39.2%), shows the least compliance with the scheduled time window (11%), mainly due to
departure delays averaging 26 min. Conversely, the booking pick-up service has the lowest
compliance rate (1%) because, on average, drivers arrived at the terminal approximately
two hours before the scheduled time window, completely missing the allocated period.
Sustainability 2025, 17, x FOR PEER REVIEW 22 of 28
Figure 10. Compliance dashboard—INT analytics.
The presented dashboards showcase the tool’s capacity to consolidate dened
indicators in a straightforward and visually appealing format. This enables stakeholders
to make beer-informed decisions, encourages proactive analysis, and facilitates an agile
response to emerging challenges and opportunities.
5.5. Analytical Insights from the Tool
The analysis conducted through the developed tool provides valuable insights into
the logistics operations of the studied terminal.
Firstly, there is a gradual increase in the advance scheduling time over the years,
although the average remains relatively low. Additionally, many appointments are
created within the time window, making proactive planning by the terminal operator
challenging.
When examining road hauler behavior, a signicant observation is that several high-
performing road haulers do not utilize prior scheduling, emphasizing the need to
encourage the broader adoption of the application. Simplied scheduling, widely used in
the early years of the analyzed data, has become less common over time. Drivers now
predominantly create appointments via the mobile application, while road hauler
managers demonstrate higher levels of responsibility, particularly in scheduling services
with greater advance time and managing cancellations.
For the haulers in which the driver makes most of the appointments, they perform
worse in terms of scheduling in advance, but comply with the time window more often.
In this respect, the third hauler with the most appointments stands out, as it constantly
scheduled the service after the start of the time window, and fullled 24% of the scheduled
services. On the other hand, in the tenth carrier with the most services, in which it is the
manager who coordinates the bookings, he created them, on average, two and a half hours
before the opening of the time window, and only 7% of the services were completed
within the time window.
Therefore, it can be concluded that scheduling the service further in advance does
not ensure that the scheduled time window is met, because drivers prioritize arriving at
the port, regardless of the scheduling. As a result, the duration of the services of the
haulers who schedule in advance is compromised.
Between 2022 and 2023, the proportion of scheduled services decreased. The delivery
service type remains the most frequently scheduled, but its signicance has been declining
Figure 10. Compliance dashboard—INT analytics.
Overall, only 21% of the bookings adhered to their scheduled time window, indicating
a clear need for improved punctuality and efficiency in fulfilling the appointment.
The presented dashboards showcase the tool’s capacity to consolidate defined indica-
tors in a straightforward and visually appealing format. This enables stakeholders to make
better-informed decisions, encourages proactive analysis, and facilitates an agile response
to emerging challenges and opportunities.
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5.5. Analytical Insights from the Tool
The analysis conducted through the developed tool provides valuable insights into
the logistics operations of the studied terminal.
Firstly, there is a gradual increase in the advance scheduling time over the years,
although the average remains relatively low. Additionally, many appointments are created
within the time window, making proactive planning by the terminal operator challenging.
When examining road hauler behavior, a significant observation is that several high-
performing road haulers do not utilize prior scheduling, emphasizing the need to encourage
the broader adoption of the application. Simplified scheduling, widely used in the early
years of the analyzed data, has become less common over time. Drivers now predominantly
create appointments via the mobile application, while road hauler managers demonstrate
higher levels of responsibility, particularly in scheduling services with greater advance
time and managing cancellations.
For the haulers in which the driver makes most of the appointments, they perform
worse in terms of scheduling in advance, but comply with the time window more often.
In this respect, the third hauler with the most appointments stands out, as it constantly
scheduled the service after the start of the time window, and fulfilled 24% of the scheduled
services. On the other hand, in the tenth carrier with the most services, in which it is the
manager who coordinates the bookings, he created them, on average, two and a half hours
before the opening of the time window, and only 7% of the services were completed within
the time window.
Therefore, it can be concluded that scheduling the service further in advance does not
ensure that the scheduled time window is met, because drivers prioritize arriving at the
port, regardless of the scheduling. As a result, the duration of the services of the haulers
who schedule in advance is compromised.
Between 2022 and 2023, the proportion of scheduled services decreased. The delivery
service type remains the most frequently scheduled, but its significance has been declining
since 2023, coinciding with an increase in the scheduling of combined delivery and pickup
services. Notably, the scheduling trends do not reflect the actual demand proportions, as
combined delivery and pickup services are the most sought after, the longest in duration,
and the least likely to meet scheduled time windows. Encouraging advance scheduling for
these critical services is essential for optimizing terminal operations.
Time window analysis reveals that the busiest windows are those with the shortest
average appointment advance times. This creates capacity management challenges for the
terminal and leads to an increased number of cancellations, often due to the expiration of
validation dates or scheduling by drivers. The afternoon period (2:00 PM–6:00 PM) is the
most in-demand, with peak truck arrivals observed between 4:00 PM and 5:00 PM. This
specific time window has the lowest appointment advance time and the lowest scheduling
proportion, with only a fraction of services fulfilled as planned.
Furthermore, services performed during this peak period experience the longest dura-
tions, contributing to delayed truck exits and further exacerbating operational pressures
in subsequent time windows. Only after 6:00 PM does the percentage of trucks complet-
ing services within the same time window stabilize at the average level, highlighting the
ongoing operational strain during peak hours.
In summary, the historical data analyzed underscores the need for more efficient
scheduling and service management practices. Implementing these improvements will
better align terminal operations with customer demands, enhance overall efficiency, and
reduce operational bottlenecks during peak periods.
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6. Evaluation: A SWOT-Analysis Overview
The business intelligence tool garnered positive feedback from key stakeholders,
who validated the construction process and results, expressing satisfaction with the tool’s
capacity to quantify and analyze previously unmeasured aspects. However, to ensure a
comprehensive assessment of the tool’s robustness and quality, it was essential to consider
several other factors.
As a result, additional evaluation criteria were considered to ensure a comprehensive
assessment of the tool. This included usability, which examines the user experience and ease
of use; functionality, which pertains to the range of analyses, information, and visualizations
provided by the dashboards; flexibility, which evaluates the tool’s capacity to adapt to
future requirements; and effectiveness, which assesses whether the tool delivers valuable
insights for decision-making.
Although feedback was collected from users during the testing phase, it is important
to note that the tool was still in its early stages, and not all suggestions could be fully
incorporated. The feedback provided valuable insights into potential improvements, but
due to the developmental phase, only some of the identified issues were addressed in the
current version. These insights will be considered for future updates of the tool.
To methodically structure this assessment, a SWOT analysis was conducted (see
Table 7). This analysis facilitated the identification of the strengths and weaknesses that
influence the tool’s success, both internally and externally.
Table 7. SWOT analysis of the developed BI system.
Positive Factors Negative Factors
Strengths Weaknesses
Internal Factors
The system measures new indicators
(%Scheduled Services, %Completion) by
integrating data from various sources and levels
of detail into a unified dashboard.
It presents data in visually appealing graphics
that enable quick interpretation.
It offers multiple functions, such as time analysis
and categorization, and enables users to compare
different categories in the graphs.
The structure and data model enable the creation
of additional dashboards as needed.
Low effort and time required to perform future
analyses, since the process is structured.
Using the interface requires some training and
understanding from the user, especially in the
dashboards that allow dynamic analysis of
various categories (Categories View).
The need for specific knowledge to configure the
tool and deal with unforeseen events.
Although the mechanism for cross-referencing
systems has achieved a representative and
crucial sample given the circumstances, this is
still limited, and it is necessary to obtain a total
correspondence between appointments and
services, which requires further integration of the
systems involved.
Opportunities Threats
External Factors
Develop the integration process between the
scheduling system and the operating system,
capitalizing on the advantages identified by the
solution built.
Make the decision-making process more
data-orientated using the tool.
Expanding the use of this type of solution to
other port terminals.
Data quality, which can undermine the credibility
of the tool’s analysis.
Resistance to adoption by users due to ignorance
of the tool’s benefits.
Power BI’s ability to handle large data volumes
may become a limitation. Increased data
complexity and volume could require additional
infrastructure to maintain performance and
efficiency.
The SWOT analysis of the solution provides a comprehensive understanding of its
transformative impact. The tool’s strength lies in its ability to seamlessly integrate data from
different sources and present it in an aesthetically appealing format, thereby facilitating
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more comprehensive and detailed analyses. The transition from a manual process to
an automated system has significantly reduced preparation time and greatly enhanced
data quality. The flexibility to generate additional dashboards and perform dynamic
analyses represents a strategic advantage. However, challenges such as the need for training
and improved system integration must be overcome to fully maximize the tool’s impact.
Feedback from real users indicated that, while the interface is generally well-received, some
users encounter difficulties with the complexity of dynamic analyses, suggesting a need
for further training. Additionally, although integration with existing systems has yielded
promising results, it remains incomplete, limiting the tool’s full potential. To enhance
broader adoption and improve efficiency, addressing these challenges through targeted
training programs and system improvements will be essential.
To better understand this impact, it is relevant to compare the situation before and after
the implementation of the BI solution. Before its introduction, data analysis was mainly
manual and involved the laborious extraction and processing of information using Excel.
This approach not only consumed significant time but also proved inefficient, leading to
prolonged task completion times and shallow analyses due to the lack of system integration.
The implementation of the solution has brought a significant transformation. The
automation and structuring of processes related to data extraction, processing, and visu-
alization have led to a significant improvement in efficiency. The new tool has equipped
users with interactive dashboards that provide a holistic perspective on indicators based
on integrated and historical data, enabling real-time analyses. This transformation has
not only reduced the time required for analysis preparation, but has also facilitated a shift
towards more strategic and value-added activities. Consequently, the solution has not
only addressed previous constraints, but has also unveiled new opportunities to address
challenges and capitalize on emerging prospects.
The impact of the tool goes beyond operational efficiency. By identifying issues related
to entry traffic, such as congestion, the solution enables the implementation of strategies
that may include limiting the number of appointments per time window, requiring a 100%
appointment rate, and optimizing truck arrival schedules. These measures contribute to
reducing congestion and, consequently, the emissions of greenhouse gases. Additionally,
the tool enables real-time analysis of operational efficiency, allowing the port to make
data-driven decisions to minimize energy consumption and logistical waste.
Looking ahead, the tool could be enhanced with the inclusion of a module dedicated
to environmental indicators. This expansion could provide deeper insights into energy
intensity, carbon emissions, and the overall ecological footprint of the port’s activities.
However, integrating such indicators requires the development of real-time data collection
systems that can track environmental parameters, such as truck energy consumption
and pollutant emissions, during port operations. This future module would not only
support sustainability, but also enable the port to meet the growing global demand for
more transparent environmental practices.
7. Conclusions
The efficient collection and processing of data is crucial for success in any business. In
today’s fast-paced world, having access to high-quality information quickly is essential for
business growth and continuity. BI tools address this need by consolidating and sharing
information in a way that is accessible and tailored to different users’ needs.
This article focused on the challenge of dispersing information within information
systems, which impacts operations and causes congestion at a port terminal. Despite the
extensive literature on the BI area, specific tools for the port sector are still under-researched.
The main goal of this work was to develop a BI tool supporting the analysis of logistics
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flows. Its application in a Portuguese seaport highlighted the value of digitalization and
BI in enabling structured and rapid analyses, reducing information gaps, and facilitating
data-driven decision-making.
Although the developed BI application met the needs of one of Portugal’s largest
container ports, the methodology applied and the solution presented offer a clear and
robust pathway for adapting the CRISP-DM methodology to the operational context of
different ports. The flexibility of CRISP-DM makes it inherently adaptable to various port
environments, where the operational models and data sources may differ. For example,
in a port with high cargo traffic and complex logistics, the data preparation phase would
need to handle large, real-time datasets from multiple systems, such as container tracking,
vehicle scheduling, and environmental monitoring. In contrast, smaller ports with simpler
operations may focus more on specific KPIs related to cargo throughput and schedule
adherence. This customization within the data preparation stage ensures that CRISP-DM
can handle both high-volume and smaller-scale data with equal efficiency.
In the data modeling phase, for instance, the methodology can be adjusted to focus
on different types of predictive models based on the port’s operational goals. Larger ports
may require predictive analytics for congestion forecasting or optimization of berth alloca-
tions, whereas smaller ports may focus on simpler dashboards that monitor operational
efficiency in real time. Additionally, the deployment phase of the solution can be tailored to
incorporate diverse port-specific software platforms (e.g., scheduling systems, sensor data
for environmental monitoring, etc.) and integrate them into a unified dashboard to provide
real-time insights. This adaptability ensures that the system can be seamlessly integrated
into various IT infrastructures.
The evaluation phase of CRISP-DM also plays a critical role in ensuring that the
solution remains relevant in diverse contexts. By continuously evaluating the impact of
the BI tool in different port environments, the methodology allows for the continuous
refinement and optimization of the solution. For example, feedback from port staff in a
specific location could lead to adjustments in the way data are visualized, enhancing user
engagement and decision-making. Thus, the CRISP-DM methodology provides a dynamic
framework that can be fine-tuned according to each port’s specific needs and operational
complexities, ensuring broad applicability across various contexts.
As a theoretical contribution, this study fills a significant gap in the literature regarding
BI tools for supporting port operations. While BI methodologies have been widely applied
in other logistics sectors, this work represents a pioneering adaptation of CRISP-DM for
maritime ports. The methodology is extended to address challenges specific to the port
environment, such as data integration from diverse, often siloed systems, and the need for
real-time analysis to support decision-making. Moreover, the flexibility of CRISP-DM to
adapt to different operational scales and contexts extends its applicability not just to large
ports, but to smaller and mid-sized terminals as well.
Developing the solution presented several challenges with limitations. Data collection
and processing were challenging due to non-conformities, which were addressed through
automated processes. However, the tool still faces challenges in autonomously handling
unforeseen non-conformities. Additionally, the absence of certain attributes in the systems
analyzed limited the sample, and the dashboard structure prioritized comprehensive
analyses over usability, potentially impacting user adherence and necessitating additional
training. Feedback from real users highlighted those certain aspects, such as the complexity
of dynamic analyses, may hinder user adoption without adequate training. Additionally, as
the solution scales, Power BI’s ability to handle larger datasets may necessitate additional
infrastructure to maintain optimal performance and efficiency.
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Future work should take a proactive approach to continuous data analysis to ensure
the BI tool remains relevant and efficient. Simplifying dashboard analysis categories can
enhance the user experience, while the development of new indicators and dashboards
must keep pace with operational evolution. The integration of real-time data can provide
more accurate and up-to-date information. Furthermore, extending the solution to other
ports and logistics sectors could broaden its impact and utility, potentially serving as a
model for other port terminals. The methodology presented here offers a clear roadmap
for adapting BI solutions to diverse port contexts, showcasing CRISP-DM’s flexibility
and potential for application across various operational environments. The solution’s
generalizability is, thus, not only feasible, but also beneficial for enhancing operational
efficiency, resilience, and decision-making in port logistics globally.
Author Contributions: Conceptualization, M.G., A.d.S. and L.T.; methodology, M.G., C.S. and L.T.;
validation, C.S., A.d.S. and L.T.; formal analysis, A.d.S. and L.T.; writing—original draft preparation,
M.G.; writing—review and editing, M.G., A.d.S. and L.T.; supervision, L.T.; funding acquisition, L.T.
All authors have read and agreed to the published version of the manuscript.
Funding: This study was funded by the PRR—Recovery and Resilience Plan and by the NextGen-
erationEU funds from the Universidade de Aveiro, within the scope of the Agenda for Business
Innovation “NEXUS: Pacto de Inovação—Transição Verde e Digital para Transportes, Logística e
Mobilidade” (project no. 53 with the application C645112083-00000059).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to privacy issues.
Conflicts of Interest: The authors declare no conflicts of interest.
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